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Publication Number:  FHWA-HRT-14-004    Date:  May/June 2014
Publication Number: FHWA-HRT-14-004
Issue No: Vol. 77 No. 6
Date: May/June 2014


Predicting Your Pavement’s Future

by Nastaran Saadatmand, Beth Visintine, and Gonzalo R. Rada

Researchers are incorporating local data and other tweaks to improve predictions of road surface performance and enhance overall asset management.

States are using today’s new models and measures of pavement performance to better manage their roadways, like this one in Washington State, for the long term.
States are using today’s new models and measures of pavement performance to better manage their roadways, like this one in Washington State, for the long term.

For State and local transportation agencies, roadway management goes beyond making decisions on pavement maintenance, preservation, rehabilitation, and reconstruction. But are the models and measures used to predict and assess pavement performance keeping pace? To achieve advances in pavement management requires an equally significant improvement in predicting and measuring performance.

Across the country, transportation agencies are improving the condition of their roadways through the use of next-generation models and measures of pavement performance. The models include those incorporated in the Mechanistic-Empirical Pavement Design Guide (MEPDG) adopted by the American Association of State Highway and Transportation Officials (AASHTO) in 2008, now available under AASHTOWare™ as the software program Pavement ME Design. Also included are models that agencies are using to better estimate maintenance needs and select preservation treatments. In addition, agencies are employing new models that assess structural condition to provide a better picture of a pavement’s health.

“When integrated with asset management programs, the new models and measures help agencies make smart decisions about managing their pavements for the long term, including when to maintain and preserve existing roads versus rehabilitating or reconstructing highway infrastructure,” says Stephen Gaj, team leader of the Asset Management Team in the Federal Highway Administration’s (FHWA) Office of Asset Management, Pavements, and Construction. “Today’s more advanced models, such as those incorporated in Pavement ME Design, are also enhancing the design of new pavements by improving the accuracy of the estimated design life.”

Agencies can use the data contained in their pavement management systems to assist in the calibration of the models in Pavement ME Design for local conditions. The local calibrations analyze data for such categories as traffic, climate, and materials, and estimate damage accumulation over the service life of a pavement. By using local data, agencies can obtain improved predictions of performance.

These ongoing advances are supported by FHWA’s Pavement Management Roadmap (FHWA-HIF-11-011). Introduced in 2010, the Roadmap outlines the research, development, and technology transfer initiatives needed through 2020 to help transportation agencies expand the role of pavement management, update the use of existing tools and technologies, and introduce new tools. Through the many projects underway or already completed, agencies are using pavement management systems more effectively, as well as other decisionmaking tools to monitor pavements, measure performance, and recommend maintenance projects and pavement preservation treatments.

MAP-21: Performance-Based Progress

FHWA developed the Roadmap during three regional workshops held in 2010 in Dallas, TX; McLean, VA; and Phoenix, AZ. Stakeholders in the workshops included representatives from State and local highway agencies, FHWA, Canadian government agencies, metropolitan planning organizations, academia, and private industry.

The advances in pavement management supported by the Roadmap received new emphasis nationwide with passage of the 2012 transportation legislation, the Moving Ahead for Progress in the 21st Century Act (MAP-21). The new law includes performance-based programs and asset management to address the challenges facing the National Highway System. These challenges include improving safety, improving or maintaining infrastructure condition, reducing traffic congestion, improving freight movement, improving air quality, and reducing delays in project completion.

One of the key features in MAP-21 is the establishment of a performance- and outcome-based strategic approach to transportation planning and investment decisionmaking. The forthcoming new approach will use information from management systems to make investment and policy decisions in support of national performance goals. “The new requirements for risk-based asset management plans and having a performance-based focus will facilitate efficient investment of Federal transportation funds, increase accountability and transparency, and improve project decisionmaking,” Gaj says.

Measuring Performance

Cover of FHWA’s Pavement Management Roadmap.
Projects supporting the goals of FHWA’s Pavement Management Roadmap are helping transportation agencies expand the role of pavement management and introduce new tools and technologies.


Cover of a report titled Ten Years of Transparency: The Role of Performance Reporting at WSDOT.
Since 2001, the Washington State Department of Transportation (WSDOT) has regularly reported on the performance of the State’s transportation system.

Achieving the Roadmap’s goals for improving pavement management begins with evaluating the current state of the Nation’s pavements. Measures of performance assess how a pavement’s current condition compares to the required level of service. These measures enable transportation agencies to identify needed maintenance and rehabilitation activities.

One of the common measures used to assess performance is the International Roughness Index (IRI). Using a device known as a road profiler, technicians measure the longitudinal profile (the changes in elevation compared to the longitudinal distance) of a pavement’s inside and outside wheelpaths. These measures are used to calculate the IRI, which represents the roughness of the pavement.

Some State transportation agencies also use indices that rate pavement condition on a graduated scale typically ranging from very poor to excellent condition categories. The New York State Department of Transportation (NYSDOT), for example, developed an index to obtain a comprehensive measure of pavement condition, including cracking and other pavement distresses. Between 2010 and 2015, NYSDOT aims to maintain at least 80 percent of New York’s interstate system roads in good or better condition.

The Michigan Department of Transportation (MDOT) collects pavement performance data on surface distresses, ride quality, and friction for every 0.1 mile (0.16 kilometer) of roadway in its network. MDOT uses the data to compile a pavement condition rating known as the Distress Index, which uses a scale that starts at zero for a pavement without any distresses and then increases as the pavement condition deteriorates. A Distress Index score of 50 or higher means that MDOT must consider rehabilitating or reconstructing the pavement.

Since 2001, the Washington State Department of Transportation (WSDOT) has catalogued the latest information on the performance of the State’s transportation system in its quarterly report The Gray Notebook. The reports feature data and analysis on topics such as bridges, construction contracts, highway system safety, asset management, and highway maintenance. As described in the WSDOT publication Ten Years of Transparency: The Role of Performance Reporting at WSDOT, “Internally, the performance measures have become a core management tool and cultural philosophy at WSDOT--the motto used often is, ‘What gets measured gets managed.’” To view current and past editions of The Gray Notebook, visit www.wsdot.wa.gov/accountability.

Evaluating the Health Of Roads and Bridges Nationwide

A 2012 pilot study on infrastructure health conducted by FHWA examined methods for reliably and consistently evaluating the health of bridges and pavements nationwide. Assessing current infrastructure condition is important to successfully implementing pavement management and other asset management systems. Focusing on the interstate system, researchers developed an approach for categorizing bridges and pavements in good, fair, or poor condition that could be used consistently across the country. Definitions of good, fair, or poor used in the study related solely to the condition of the bridge or pavement and did not consider other factors such as safety or capacity.

The study, documented in Improving FHWA’s Ability to Assess Highway Infrastructure Health: Pilot Study Report, evaluated three tiers of performance measures previously defined by AASHTO that could be used to categorize bridges or pavements. Tier 1 measures are considered ready for use at the national level, while tier 2 measures require further work before being ready for deployment. Tier 3 measures generally are still in the proposal stage. Performance measures for bridges included structural deficiency ratings (tier 1) and structural adequacy based on National Bridge Inventory (NBI) ratings (tier 2). FHWA’s NBI database contains a range of data on bridges in the United States, including design, specification, and State inspection information. A tier 3 measure was not included for bridges.

Software screenshot. Data filled in include project information, GPS coordinates, existing pavement thickness, and graphs that illustrate the data. Data included are for illustration purposes only.
Shown here are sample data screens from software developed by Louisiana that uses new models to help users select more cost-effective treatments for pavement rehabilitation and maintenance. (Data included are for illustration purposes only.)

Performance measures for pavements included IRI (tier 1) and functional adequacy based on distress data from the Highway Performance Monitoring System (HPMS) (tier 2). Developed in 1978, FHWA’s HPMS database includes data on the condition, performance, and use of the Nation’s highways. The proposed tier 3 measure for pavements assesses structural condition based on tier 2 data and pavement deflection data.

The FHWA study evaluated these measures on I–90 in Minnesota, South Dakota, and Wisconsin. This corridor stretches for 874 miles (1,407 kilometers), with average annual daily traffic ranging from approximately 5,000 to 90,000 vehicles. The FHWA study team conducted the evaluations using HPMS and NBI data, as well as data provided by the Minnesota, South Dakota, and Wisconsin departments of transportation and new data collected by the team using a multifunctional automated pavement data collection vehicle. State data included records from corridor inventories and pavement management systems.

The study’s good, fair, and poor analysis proved to be a viable approach for both bridges and pavements. Study researchers also recommended further development of the proposed tier 2 measure for bridges and tier 2 and tier 3 measures for pavements to more fully represent infrastructure condition.

As part of the study, researchers developed a sample health report for the pilot corridor. This report used several metrics to assess the overall health of the corridor, including the good/fair/poor measures, age, remaining service life for pavements, and traffic volumes. Developing such reports in the future would enable FHWA to analyze corridor health across multiple States in a consistent manner.

To obtain a copy of the study report, Improving FHWA’s Ability to Assess Highway Infrastructure Health (FHWA-HIF-12-049), visit www.fhwa.dot.gov/asset/pubs/hif12049/hif12049.pdf.

A Next-Generation Performance Measure

After concluding the infrastructure health study, FHWA conducted further research that concentrated on developing a next-generation pavement performance measure that would provide an accurate and repeatable assessment of a roadway’s condition. During this followup study, the focus shifted from developing a single composite measure of ride quality, cracking, and rutting or faulting to instead measuring and analyzing these distresses individually. The researchers conducted a field validation for the study in May 2013 along the I–90 corridor in Minnesota.

To view the study’s recommendations for collecting, processing, reviewing, and storing data on each of these individual pavement distresses to evaluate pavement performance, visit www.fhwa.dot.gov/asset/pubs/hif13042.pdf.

Pavement Performance Models

Performance models are also key to improving pavement management. Transportation agencies use pavement performance models to assess the deterioration of a pavement over time and predict future performance. Network-level performance models can be used to select optimal maintenance and rehabilitation strategies, while project-level performance models can assist in making decisions about pavement design. Mechanistic-empirical models adopted by Pavement ME Design use both mathematical models and experimental data on inservice pavements to predict performance.

A pilot project on I–81 in Virginia, shown here, demonstrated that the Virginia Department of Transportation’s (VDOT) new Modified Structural Index can help improve the process for selecting pavement sections for maintenance.
A pilot project on I–81 in Virginia, shown here, demonstrated that the Virginia Department of Transportation’s (VDOT) new Modified Structural Index can help improve the process for selecting pavement sections for maintenance.

Currently, agencies are expanding their pavement management capabilities by developing advanced models that more accurately assess roadway conditions and predict performance. Among them are those that model the structural condition of pavements and others that consider a series of preservation treatments and make recommendations based on cost-effectiveness. In projects conducted by FHWA and States across the country, these new models are helping agencies improve the condition of pavements.

Louisiana: Treatment Performance Models

“Development of Cost-Effective Pavement Treatment Selection and Treatment Performance Models,” a study sponsored by the Louisiana Department of Transportation and Development (LA DOTD) and the Louisiana Transportation Research Center, has resulted in new models for selecting a range of preservation treatments. Among the treatments are chip seals, crack seals, microsurfacing, and both thin and thick hot-mix asphalt overlays.

“Pavement performance models for each type of pavement distress and each pavement treatment have been developed,” says study researcher Mohammad Jamal Khattak of the University of Louisiana at Lafayette.

Researchers now are designing a comprehensive software package that can be integrated into LA DOTD’s existing pavement management system. Scheduled to be completed in 2014, the software will include databases of applied treatment types, distresses, weather conditions, and treatment costs and triggers, along with the performance models for each type of treatment and distress.

“Highway engineers will be able to use the new software to determine the optimum pavement treatment type and timing based on life-cycle cost analyses,” says Khattak. “The software will serve as an effective tool for improving pavement preservation and increasing the effectiveness of pavement management systems.”

Texas: Pavement Management Information System

The Texas Department of Transportation (TxDOT) uses an electronic Pavement Management Information System to monitor performance across its road network and make decisions about allocating rehabilitation and maintenance funds. A project sponsored by TxDOT and FHWA developed new models that predict performance, better estimate maintenance needs, and reduce errors in predicting performance. Previously, performance models predicted that pavement distresses would occur more rapidly than observed by TxDOT staff monitoring the actual performance of pavements.

The researchers calibrated the new models using data on conditions for the different pavement types employed by TxDOT (hot-mix asphalt, continuously reinforced concrete, and jointed plain concrete) and the range of climate, pavement subgrade, and traffic conditions encountered across the State. These updated models enhance the value of the electronic Pavement Management Information System as a decision support tool. To view the 2012 final project report, Evaluation and Development of Pavement Scores, Performance Models, and Needs Estimates for the TxDOT Pavement Management Information System, visit http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/0-6386-3.pdf.

Photo. Shown here is an orange van pulling a trailer-mounted falling weight deflectometer along a stretch of highway.
An FHWA study developed simple techniques for using falling weight deflectometers (FWDs) like this one to assess pavement structural capacity. Combining the structural data from the FWD analysis with measurements of pavement condition, such as cracking, results in more effective preservation and rehabilitation programs.

A followup project conducted by FHWA and TxDOT introduced the new models to that State’s engineers and managers during two webinars. In followup surveys, webinar participants highly rated the accuracy of the new models, based on practical experience. More information is available in the project report, Implementation of New Pavement Performance Prediction Models in PMIS [Pavement Management Information Systems], which is available at http://ntl.bts.gov/lib/46000/46600/46638/5-6386-01-1.pdf.

Virginia: Selection Process For Pavement Maintenance

Some next-generation performance models also consider the structural condition of a pavement. In Virginia, for example, a new Modified Structural Index (MSI) for asphalt pavements shows the impact of the structural condition of the pavement on the expected service life of a pavement maintenance treatment. The data on structural condition give pavement engineers a better picture of pavement health than is possible by monitoring observable pavement conditions.

Researchers at the Virginia Tech Transportation Institute’s Center for Sustainable Transportation Infrastructure developed the MSI under a contract with the Virginia Center for Transportation Innovation and Research, the research division of the Virginia Department of Transportation (VDOT). A modified version of the TxDOT Structural Condition Index, the MSI minimizes discrepancies among predictions about the performance of pavement treatments based on data from an overall road network and decisions made at the individual project level.

The research team designed the MSI to supplement VDOT’s current database of historical condition and construction data for more than 125,000 lane-miles (201,168 lane-kilometers) of roads throughout the State. In 2006, the agency also began collecting distress data using digital images. VDOT uses a set of pavement management decision matrices to plan treatments. The matrices are classified by interstates, primary routes, secondary routes, and unpaved roads, as well as by pavement type.

“The new index will allow Virginia to consider structural condition in addition to observable pavement condition to improve the process for selecting pavement sections for maintenance,” says Brian Diefenderfer, a senior research scientist at the Virginia Center for Transportation Innovation and Research. “The data in the index will also improve the selection of pavement treatments.”

A pilot implementation project on I–81 in Virginia demonstrated that the MSI can be used to support the process of making pavement management decisions, including deterioration modeling and the development of structural performance measures.

Researchers now are developing a similar index for both concrete and composite (combined asphalt and concrete) pavements. The second phase of the project is scheduled to be completed in 2014. To download the 2013 project report, Developing a Network-Level Structural Capacity Index for Structural Evaluation of Pavements, visit www.virginiadot.org/vtrc/main/online_reports/pdf/13-r9.pdf.

Assessing Structural Performance

Several FHWA projects have examined how researchers can analyze data on a pavement’s structural capacity more easily to manage roadways and make maintenance decisions. A study on the “Development of a Simplified Method for Interpreting Surface Deflections for In-Service Flexible Pavement Evaluation,” for example, resulted in a method to evaluate deterioration in asphalt pavements and provide estimates of remaining structural capacity. The method uses data collected from a falling weight deflectometer (FWD), which imparts a dynamic load to the pavement surface that is similar to that of a single heavy moving wheel load. The resulting pavement deflection can then be measured and the data used to help analyze the remaining service life of a pavement.

The FHWA study also used data from pavement test sections in the FHWA Long-Term Pavement Performance program database to recommend simplified techniques for using FWDs to assess pavement structural capacity.

Combining the structural data from the FWD analysis with measurements of pavement condition, such as cracking and roughness, enhances decisionmaking and results in a more effective preservation and rehabilitation program. In the FHWA study, for example, incorporating the new structural analysis changed the prescribed treatments for about 60 percent of the study’s sections.

Although many viable techniques exist for evaluating the structural capacity of pavements using FWD data, most of these techniques are time consuming and require an experienced analyst. Following its study, FHWA issued a new guide that presents a simpler approach, including information on when and how often to conduct FWD testing.

“The guidelines raise awareness of how to perform structural analysis to better manage pavement infrastructure,” says Larry Wiser, a highway research engineer at FHWA’s Turner-Fairbank Highway Research Center in McLean, VA. “They are both reliable and easy to incorporate into current pavement management system practices.”

To download a copy of Simplified Techniques for Evaluation and Interpretation of Pavement Deflections for Network-Level Analysis: Guide for Assessment of Pavement Structure Performance for PMS [Pavement Management System] Applications (FHWA-HRT-12-025), visit www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/12025/index.cfm.

Collecting Data at Highway Speeds

New devices, such as rolling wheel deflectometers (RWDs), which can collect pavement deflection data while traveling at highway speeds, offer the opportunity to predict structural condition for improving pavement management, while minimizing traffic disruptions and increasing safety for highway workers. An RWD imparts a dynamic load to the pavement surface similar to that of an FWD, but it can be used at highway speeds. An FHWA project, “Pavement Structural Evaluation at the Network Level,” evaluated available devices, including examining their operating speeds and the accuracy and precision of their measurements. FHWA and seven States are now conducting a pooled fund research study to further assess feasibility and demonstrate the use of traffic speed deflectometers.

Photo. Three trucks are shown on a segment of the MnROAD adjacent to a pond. As part of an FHWA study, the trucks to the right and left collected pavement deflection data at highway speeds. The middle truck was used to collect data at a lower speed.
The trucks to the right and the left in this photo collected data on pavement deflection at highway speeds during an FHWA study conducted at Minnesota’s MnROAD pavement test track in September 2013. Collecting the data at highway speeds helps agencies improve pavement management, while minimizing traffic disruptions and increasing safety for highway workers.

As part of its Pavement Structural Evaluation project, FHWA tested two pavement deflection devices at highway speeds in September 2013 at Minnesota’s MnROAD pavement test track. “The evaluations went well,” says Nadarajah Sivaneswaran, a senior research civil engineer at FHWA’s Turner-Fairbank Highway Research Center in McLean, VA. “We will now be analyzing the data to assess each of the device’s technical capabilities and develop analysis methodologies. The new analysis methodologies will focus on how States can effectively use information obtained from these devices in their pavement management systems to make better pavement investment decisions.”

Join the Next Generation

From assessing the condition of a pavement more accurately to making smarter decisions about the most effective treatment strategy, the advances in pavement management resulting from new performance models and measures ultimately will result in a more durable, better performing, and safer roadway network. To learn more about next-generation strategies for pavement management and the many new tools and technologies available today, as well as the ongoing FHWA and State research projects that continue to expand the capabilities of pavement management, visit FHWA’s Pavement Management Roadmap Web site at www.fhwa.dot.gov/pavement/management/roadmap or www.fhwa.dot.gov/asset/hif11011/map00.cfm.

Nastaran Saadatmand is the asset management program manager in FHWA’s Office of Asset Management, Pavements, and Construction in Washington, DC. She has more than 24 years of experience in highway engineering. Saadatmand holds a B.S. in mechanical engineering from the Iran University of Science and Technology in Tehran, an M.S. in civil engineering from the University of New Hampshire, and a professional engineer’s license from the State of New Hampshire.

Beth Visintine is a project engineer for AMEC Environment & Infrastructure, Inc. She holds a B.S., M.S., and Ph.D. in civil engineering from North Carolina State University.

Gonzalo R. Rada is a principal engineer for AMEC Environment & Infrastructure, Inc. He holds a B.S., M.S., and Ph.D. in civil engineering from the University of Maryland, College Park, and is a registered professional engineer in five States.

For more information, visit the Pavement Management Roadmap at www.fhwa.dot.gov/pavement/management/roadmap or www.fhwa.dot.gov/asset/hif11011/map00.cfm. Or contact Nastaran Saadatmand at 202–366–1337 or nastaran.saadatmand@dot.gov, Beth Visintine at 301–210–5105 or beth.visintine@amec.com, or Gonzalo R. Rada at 301–210–5105 or gonzalo.rada@amec.com.




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